1. technology and computing
2. software
3. databases

Transaction Processing over
High-speed Networks
Proposal by:
Research Committee:
Erfan Zamanian
Tim Kraska
Maurice Herlihy
Stan Zdonik
[email protected]
{tim_kraska, mph, sbz}@cs.brown.edu
Brown University
April 2015
Abstract
By avoiding high cost of disk I/O, memory-resident OLTP databases reduce the runtime of typical singlesited transactions to a fraction of a millisecond. With disk gone from the picture, network has become
the next bottleneck. In fact, the traditional wisdom is that network is the new disk, and distributed
transactions must be avoided as much as possible, through techniques such as partitioning. However,
recent technological trends toward low-latency, high-bandwidth networks which are capable of minimizing
communication overhead through Remote Direct Memory Access (RDMA) are changing our view on
the fundamental assumption that network is significantly the bottleneck. For example, the latency in
InfiniBand FDR/EDR is less than 1µs, two orders of magnitude faster than the traditional 1Gb Ethernet,
and is only one order of magnitude slower than local memory access (0.1µs).
In this project, we aim to explore the design of a memory-resident OLTP with optimistic concurrency
control, in particular snapshot isolation, which leverages RDMA and low latency of fast networks as
much as possible. We argue that the new RDMA-enabled architecture is neither shared-memory nor
shared-nothing. This new architecture requires us to fundamentally rethink the design of databases. In
particular, we plan to address the question that in an RDMA-capable distributed system, what changes
have to be made to concurrency control and related components of these databases. We speculate that
benefits of using RDMA in OLTP systems are three-fold; first, it decreases the latency of transactions.
Second, in this new hybrid architecture, the burden of transaction processing can be potentially shared
between servers and clients, resulting in a more balanced design. Finally, separating transaction processing
from data management, which are both traditionally handled by the server, could facilitate the scale-out
process. Our initial experiments show that exploiting RDMA could result in at least an order of magnitude
higher performance compared to the existing design.
I.
Introduction
he notion of transaction is one the most
fundamental concepts in database management systems. Online Transaction
Processing (OLTP) databases rely on concurrency control and other mechanisms to create
T
the illusion for each transaction that it is alone
in the system, while actually there might be
concurrent transactions being run simultaneously. Besides, transactions are guaranteed to
be deterministic, meaning that their effects persist over time, and they are executed in their
entirety, transforming the database from one
1
consistent state to another. In a distributed
database, transactions may span multiple machines. In order to allow concurrent execution,
coordination between the involving nodes over
the network is required. For example, in a
money transfer between user A and B, whose
records are hosted at different machines, whatever amount is deducted from A’s account at
site S1 must be transferred to B’s account at site
S2. S1 and S2 must both agree on the verdict
of this transaction. Such agreement involves
coordination between the two nodes over the
network.
Modern distributed main-memory DBMSs
are built on the assumption that network communication is slow and network bandwidth is
severely limited. Table 1 shows a comparison
between different components of a computer
system. By getting rid of disk as the main
storage, memory-resident OLTP systems have
reduced the run-time of transactions, which
are typically short-running and do not need
complex computation, to less than 100µs [4, 5],
if no network is involved. Distributed transactions, on the other hand, often require multiple
network round-trips. For example, the standard version of two phase commit requires 4
end-to-end communications between the initiator and participants, which can take up to
400µs, 4 times the actual work of a local transaction. This added overhead not only increases
the transaction latencies, but also increases the
chance of contention over locks. As the contention rate rises, it becomes more likely for
transactions to abort.
Therefore, it is easy to see why in-memory
OLTP systems avoid distributed transactions as
much as possible. They achieve this by using
complex partitioning schemes to co-locate data
to make sure that transactions can access all the
data they need in a single site, such that no coordination between sites is needed. While this
is a solution, it imposes a new set of challenges
for the developer. Besides, some workloads,
such as social-graph data, are inherently not
perfectly partitionable.
The assumption that network is by far the
slowest component of distributed systems and
2
must be avoided at all cost, has started to become false. High-performance Remote Direct
Memory Access (RDMA) capable networks,
such as InfiniBand FDR/EDR, which were previously only used in High Performance Computing (HPC) environments due to their high
cost, have started to gain economic viability
and find their ways to datacenters. Table 1
shows the latency and bandwidth of two generations of InfiniBand networks. As can be seen,
their latency is only one order of magnitude
higher than local main memory random access, as opposed to three orders of magnitude
in traditional TCP/IP Ethernet-based message
passing. In addition to its low latency, RDMA
completely bypasses the traditional network
stack and the kernel of sender and receiver, allowing a machine to transfer data to and from
another machine’s memory with little or no
involvement of the CPU on the remote side.
Incorporating RDMA, however, requires a
re-design in many components of OLTP systems. This can be best explained by contrasting the existing distributed OLTP architectures, particularly the prevalent design, namely
shared-nothing, with the new architecture. Figure 1 depicts a simple representation of these
architectures.
In a shared-nothing architecture, server’s
CPU is the single point of coordination in handling client’s requests. It has exclusive access
to its own data residing in the main memory.
It receives the request, processes it, and returns
the result to the client.
An RDMA-enabled architecture, on the
other hand, is neither a pure shared-nothing design, where message-passing is the only means
of communication, nor a pure shared-memory
architecture due to three main reasons. First,
the memory access patterns are quite different
in RDMA and NUMA architecture. Second,
the latency between machines is significantly
higher to access a random byte than with today’s NUMA systems. Third, in a NUMA architecture, hardware-embedded coherence protocols ensure data consistency, which is not
supported with RDMA, since data is always
copied.
Disk (SSD)
Main Memory (DDR3)
1 Gb Ethernet
InfiniBand 4xFDR
InfiniBand 4xEDR
End-to-end Latency (µs)
10,000
0.1
100
0.7
0.5
Throughput (GB/s)
0.3
12.8
0.125
6.8
12.1
Table 1: Comparison of data transfer in different computer components
Figure 1: Different Architectures of databases
This hybrid architecture, where communication is possible through both message passing and direct memory access, requires a complete re-design of OLTP systems.
II.
Background
Among the various RDMA-enabled interconnects, InfiniBand has gained significant acceptance in the HPC community due to its ultra low latency and high bandwidth. While it
used to be very expensive in the past, it has recently become cost-competitive with Ethernet,
and therefore could be seen as a viable alternative as interconnect in datacenters. InfiniBand implements many layers of the network
stack, from transport to physical layer, directly
in NICs. In this section, we will give a brief
overview of the RDMA technology.
I.
RDMA
The application sends or receives request directly to and from its RDMA NIC (RNIC) using
the RDMA API, which is based on verbs. Invoking this process does not involve any system
call and context switch from the user space
to the kernel space. As most operating systems impose high overheads for interrupt processing, network protocol stacks, and context
switching, avoiding them significantly helps
decrease the latency.
There are two communication models: onesided communication and two-sided communication.
One-sided, or memory semantic verbs, are
those verbs which are performed without any
knowledge of the remote side. RDMA READ,
WRITE, and atomic operations, such as Compare and Swap (CS), and Fetch and Add (FA)
are one-sided operations. The active side submits the verb, while the passive side is completely unaware of the process. Both active
and passive sides must register the memory
region to be able to access it via RDMA. The
passive side’s RNIC directly writes/fetches the
desired data using an DMA operation from
local memory.
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Two-sided, or channel semantic verbs,
such as SEND and RECEIVE, require both sites
to involve in the communication process. The
payload of the SEND is written to the memory
region specified by a corresponding RECEIVE
which must be posted by the receiver before
the sender actually sends its request. Therefore,
the sender’s RNIC does not have to register the
remote memory region before performing the
operation.
While two-sided verbs are workflow-wise
similar to socket programming semantics,
namely read() and write(), leveraging the onesided verbs, such as READ and WRITE, requires a dramatic change in the programming
model.
RDMA uses the concept of queue pairs for
connections. Application posts verbs to the
send queue, which has a corresponding receive
queue on the receive side, so the name queue
pair. Once RNIC performs the requested memory access at the remote side, it pushes a completion event on a corresponding completion
queue, which can notify the sender about the
completion of the task. All queues are maintained inside RNIC.
III.
Proposal
We argue that there is a need to fundamentally
rethink the entire OLTP databases, including
data access patterns, replication, log management, etc. to take full advantage of the next
generation of network technology.
In this work, we will explore the design of
an RDMA-aware OLTP database for the new
network, where the transaction logic is pushed
as much as possible to clients. Our focus will
be primarily on optimistic concurrency control,
in particular snapshot isolation. In the most
extreme case, where all requests are handled
directly by clients, and servers are not involved
in transaction processing. As such, servers are
only responsible for managing their own data
and keeping it “clean”, such as garbage collection.
In particular, throughout the course of this
project, we would like to address the following
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questions:
• Given that some workloads are inherently hard or impossible to partition, is it
still the case that distributed transaction
must be avoided at all cost.
• In what aspects does the design of a pure
RDMA OLTP system with snapshot isolation differ from a traditional requesting
client/responding server architecture?
• Now that clients can access server’s data
directly in the new design, how does the
server’s memory should be organized for
efficient access.
• Clients must know the exact location of
records and their (possibly) multiple versions in order to access it via RDMA.
How should the catalog management be
done in this environment? In particular,
making the clients aware of new data arrival (e.g. SQL UPDATE and INSERT)
and deletion (e.g. SQL DELETE) is of
especial interest.
• RDMA operations lack richness. Anything which is more complicated than
a simple memory lookup or memory
write has to pay for multiple network
roundtrips, and could easily offset all
the advantages of using RDMA. The research question here is how to use RDMA
efficiently to minimize the number of
netwrok roundtrips.
• Without any single coordinator for memory accesses, write-write and read-write
races can occur. How to avoid them?
• The search space within RDMA itself
is rather large. For instance, there are
a handful of verb semantics (including
channel semantics and memory semantics), transport types (including reliable,
unreliable and unconnected) and other
important parameters (e.g. signalling, request and response sizes, etc.) that make
the design of an RDMA-aware system
non-trivial. For OLTP systems, we are
interested to explore the optimal combination of these factors that results in high
performance.
The main focus of this project is to explore
the design of a pure RDMA-enabled OLTP
system. However, we believe that the highest performance is achieved neither in a pure
messaging system nor in a pure RDMA one.
The optimal design relies somewhere between
these two ends of the spectrum, where clients
and servers divide the transaction processing
logic between themselves. Finding such a design is left as the stretch goal.
IV.
Preliminary Results
V.
Project Schedule
In the remaining 10 months of the project, we
will first complete our literature review, both
on related transaction processing papers and
RDMA related papers. We plan to divide the
remaining time between system design, and
system implementation.
In the first half, we are first going to design
and run some micro benchmarks on RDMA
verbs in InfiniBand. The goal here is to fully
understand how various RDMA choices compare to each other, recognize their characteristics, and find out what communication scheme
is more suitable for different parts of our system. We will then continue with the actual
system design. Here, we aim to address all the
questions mentioned in Section III. The second
half will be devoted to implementation of our
proposed system. Table 2 shows the time table
for the project schedule.
VI.
Related Work
This work combines the ideas of the wellstudied topic of distributed transaction processing with the emerging technology of RDMAcapable fast networks.
I.
Distributed Transaction Processing
[4] performed a detailed study of performance
overhead of different components in a distributed OLTP database. After breaking down
the time that the server’s CPU spends on various components of transaction processing, they
concluded that buffer management, logging
and locking significantly increase the overhead
and should be reduced/removed as much as
possible. In a related work, [13] argues how
the need for exploiting recent technological
advances in hardware (main memory in their
case) could require a fundamental re-design of
the entire system.
II. RDMA-based Data Management
Systems
Although RDMA has attracted a lot of attention in the Systems community (e.g. for building distributed lock managers [1, 10], replicated state machines [11], RDMA-based of existing systems such as Hadoop [8]), it is a relatively unexplored topic in the area of databases.
FaRM [2] is a general-purpose main memory
distributed computing platform which exploits
RDMA to improve latency. Similar to our work,
it provides ACID guarantees for local and distributed operations. However, it is not an OLTP
database and its API is quite limited. FaRM
achieves high throughput by using one-sided
RDMA WRITE to implement a fast message
passing primitive, and RDMA READ for readonly operations. Since access local memory is
still much faster than RDMA, FaRM performs
locality-aware optimizations.
There has been a number of works that
leverage RDMA to build key-value stores
[9, 6, 7]. In Pilaf [9], while get() are handled by
having clients directly access the server’s memory using RDMA READ, put() requests are
sent to and serviced by the server via RDMA
WRITE. Since clients are not involved in put(),
the synchronization process between concurrent memory accesses is much more simplified. In this design, put() is performed in one
roundtrip (one RDMA WRITE for sending the
request and one for the response), while get()
5
Task
Literature study
RDMA micro benchmarks
System design
Implementation
Experiments
Finishing up report
Time Required (month)
1
1
3
3
1
1
Target Completion Date
6-1-2015
7-1-2015
10-1-2015
1-1-2016
2-1-2016
3-1-2016
Table 2: Project schedule
requires at least two RDMA READ (one for
looking up a key in the hash table array to find
the memory address of the key-value pair on
the server, and one to fetch the actual value
associated with the key). As a critique on the
aforementioned design, [6] argues that a system which requires multiple roundtrip of onesided RDMA operations (as in the case of Pilaf,
RDMA READ) could be outperformed by a
design where RDMA verbs are used merely as
a message-passing mechanism, and not memory access. Therefore, in their system, HERD,
the client writes its get() or put() request into
the server’s memory, and the server computes
the reply. They showed that the maximum
throughput can be achieved by bypassing network stack and CPU interrupts, and not bypassing the CPU entirely.
Due to its high bandwidth, fast networks
have gained attention for data analytics. Traditionally, there used to be a large gap between
the bandwidth of network and intra-processors,
which led to designs of CPU-intensive algorithms for query processing, which would
avoid the network traffic as much as possible.
The authors in [12] argue that fast networks
have started to change this assumption, and
proposed a distributed query engine which optimizes the exchange operator for bulk transfer
using RDMA. [3] showed how RDMA could
be leveraged to boost the performance of join.
References
[1] A. Devulapalli and P. Wyckoff. Distributed queue-based locking using advanced network features. In Parallel Pro6
cessing, 2005. ICPP 2005. International Conference on, pages 408–415. IEEE, 2005.
[2] A. Dragojevic, D. Narayanan, M. Castro,
and O. Hodson. Farm: Fast remote memory. In Proceedings of the 11th USENIX
Symposium on Networked Systems Design
and Implementation, NSDI 2014, Seattle, WA,
USA, April 2-4, 2014, pages 401–414, 2014.
[3] P. W. Frey, R. Goncalves, M. Kersten, and
J. Teubner. A spinning join that does not
get dizzy. In Distributed Computing Systems (ICDCS), 2010 IEEE 30th International
Conference on, pages 283–292. IEEE, 2010.
[4] S. Harizopoulos, D. J. Abadi, S. Madden,
and M. Stonebraker. OLTP through the
looking glass, and what we found there.
In Proceedings of the ACM SIGMOD International Conference on Management of Data,
SIGMOD 2008, Vancouver, BC, Canada, June
10-12, 2008, pages 981–992, 2008.
[5] E. P. Jones, D. J. Abadi, and S. Madden.
Low overhead concurrency control for partitioned main memory databases. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data,
SIGMOD ’10, pages 603–614, New York,
NY, USA, 2010.
[6] A. Kalia, M. Kaminsky, and D. G. Andersen. Using rdma efficiently for key-value
services. SIGCOMM Comput. Commun.
Rev., 44(4):295–306, Aug. 2014.
[7] H. Lim, D. Han, D. G. Andersen, and
M. Kaminsky. Mica: A holistic approach
to fast in-memory key-value storage. management, 15(32):36, 2014.
[8] X. Lu, N. S. Islam, M. Wasi-ur Rahman,
J. Jose, H. Subramoni, H. Wang, and
D. K. Panda. High-performance design
of hadoop rpc with rdma over infiniband.
In Parallel Processing (ICPP), 2013 42nd International Conference on, pages 641–650.
IEEE, 2013.
[9] C. Mitchell, Y. Geng, and J. Li. Using
one-sided rdma reads to build a fast, cpuefficient key-value store. In USENIX Annual Technical Conference, pages 103–114,
2013.
[10] S. Narravula, A. Mamidala, A. Vishnu,
K. Vaidyanathan, and D. K. Panda. High
performance distributed lock management services using network-based remote atomic operations. In Cluster Computing and the Grid, 2007. CCGRID 2007.
Seventh IEEE International Symposium on,
pages 583–590. IEEE, 2007.
[11] M. Poke and T. Hoefler. DARE: HighPerformance State Machine Replication
on RDMA Networks. ACM, Jun. 2015.
Accepted at ACM HPDC’15.
[12] W. Rödiger, T. Mühlbauer, A. Kemper,
and T. Neumann. High-speed query processing over high-speed networks. arXiv
preprint arXiv:1502.07169, 2015.
[13] M. Stonebraker, S. Madden, D. J. Abadi,
S. Harizopoulos, N. Hachem, and P. Helland. The end of an architectural era (it’s
time for a complete rewrite). In Proceedings of the 33rd International Conference on
Very Large Data Bases, University of Vienna,
Austria, September 23-27, 2007, pages 1150–
1160, 2007.
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